get_enrichment

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool performs enrichment analysis but doesn't describe what the analysis entails (e.g., statistical methods, output format, potential rate limits, or authentication needs). For a tool with 3 parameters and no annotations, this is a significant gap in explaining how the tool behaves beyond its basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and examples. Every sentence earns its place by conveying essential information without redundancy or unnecessary detail, making it efficient for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (functional enrichment analysis with 3 parameters), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage context, which are crucial for an AI agent to effectively invoke this tool. The description does not compensate for the absence of structured data, leaving significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents the parameters (identifiers, species, background). The description adds no additional meaning beyond what the schema provides—it doesn't explain parameter interactions, typical values, or usage examples. Baseline 3 is appropriate as the schema handles the heavy lifting, but the description offers no compensatory insights.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Perform functional enrichment analysis for a set of proteins' with specific examples of what it tests (Gene Ontology terms, KEGG pathways). It uses a specific verb ('perform') and identifies the resource ('proteins'), but doesn't explicitly differentiate from sibling tools like 'get_ppi_enrichment' or 'get_network' which might involve similar biological data analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions what the tool does but doesn't indicate scenarios for its use, prerequisites, or comparisons with sibling tools like 'get_ppi_enrichment' or 'get_network' that might handle related protein analyses. This leaves the agent without context for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.